After the best model is constructed from lightweight ML blocks through NAS, the model is ready for deployment onto microcontrollers. This is performed by TinyML software suites, which generate embedded code and perform operator and inference engine optimizations, some of which are shown in Fig. REF . Notable open-source TinyML frameworks and inference engines include TensorFlow Lite Micro [1]}[2]}, uTensor [3]}, Microsoft EdgeML [4]} [5]}[6]}[7]}, [8]}, [9]}, [10]}, CMSIS-NN [11]}, EloquentML [12]}, Sklearn Porter [13]}, EmbML [14]}, and FANN-on-MCU [15]}. Closed-source TinyML frameworks and inference engines include STM32Cube.AI [16]}, NanoEdge AI Studio [17]}, Edge Impulse [18]}, TinyEngine [19]} [20]}, Qeexo AutoML [21]}, Deeplite Neutrino [22]}, Imagimob [23]}, Neuton TinyML [24]}, Reality AI [25]}, and SensiML [26]}. Table REF  compares the features of some of these frameworks.
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